[1]WU Guodong,ZHA Zhikang,TU Lijing,et al.Research advances in graph neural network recommendation[J].CAAI Transactions on Intelligent Systems,2020,15(1):14-24.[doi:10.11992/tis.201908034]
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Research advances in graph neural network recommendation
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 1
Page number:
14-24
Column:
综述
Public date:
2020-01-05
- Title:
- Research advances in graph neural network recommendation
- Keywords:
- graph neural network; recommendation system; deep learning; entity relations; society relation; collaborative filtering; undirected graph; directed graph
- CLC:
- TP301
- DOI:
- 10.11992/tis.201908034
- Abstract:
- Graph neural network (GNN) has the advantage of feature extraction and representation of data from the field of Graph. In recent years, it has become a hotspot of artificial intelligence research, and the recommendation of Graph Neural Network is also a new direction of recommendation system research. Based on the in-depth study of GNN model, this paper analyzes the GNN recommendation process, and discusses in detail the main progress and deficiencies of GNN recommendation studies from three aspects: undirected unit graph recommendation, undirected binary graph recommendation and undirected multivariate graph recommendation. The main difficulties in existing GNN recommendation studies are clarified, and the research directions of GNN recommendation in the future, including GNN contextual recommendation, GNN cross-domain recommendation, GNN group recommendation, and GNN recommendation’s interpretability, and so on, are pointed out in the end.
- References:
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1900-01-01